The complexity of the game is high compared to Chess or Go. While for Chess the turn-wise branching factor is of \(\approx 30\)  and \(\approx 300\)  for Go, for Blood Bowl it is \(\approx 10^{50}\) . As a result, the authors of the FFAI framework reports that in 350k games, a random bot could never score a touchdown when playing against another random bot.
The high branching factor makes it particularly challenging to adapt existing algorithms to work in this setting, especially if they are search based.
At the same time, the inability of a random bot to perform meaningfully well, the high degree of randomness and the fact that every mistake is punished harshly by interrupting the player turn, makes it difficult to employ learning paradigms based on trial and error.
Reinforcement Learning using A2C [1]} with Curriculum Learning [2]} was previously applied in the Bot Bowl II competition, but the resulting bot could win only 70% of the time against a random bot, while it could not beat a scripted bot that has roughly the performance of a novice human player.
